Machine learning algorithms, particularly those based on pattern recognition, have become indispensable tools for aiding in the diagnosis, treatment, and prediction of outcomes in neurological diseases. The evaluation and management of AIS have witnessed significant advancements, necessitating the integration of neuroimaging data into decision-making processes. This abstract aims to provide an overview of recent developments and applications of ML in neuroimaging, with a specific emphasis on its role in acute ischemic stroke. By leveraging ML techniques, neuroimaging data can be effectively analyzed and interpreted to enhance diagnostic accuracy, treatment selection, and prognostic evaluation in AIS. This review sheds light on the potential benefits and implications of ML in acute ischemic stroke neuroimaging, demonstrating its ability to augment clinical decision-making and ultimately improve patient care in this critical domain. Pattern recognition algorithms based on machine learning (ML) are now playing a crucial role in assisting with the diagnosis, treatment, prediction of complications, and patient outcomes in various neurological conditions. The management of Acute Ischemic Stroke (AIS) has significantly progressed in recent years, with an increasing reliance on neuroimaging for informed decision-making. In this review, we provide an overview of the recent advancements and utilization of ML in neuroimaging, with a specific focus on acute ischemic stroke.